The invention relates to a method for processing an image sequence, involving ascertaining a change of position, in a current image, for objects depicted in said image with respect to a reference image. The inventors also propose a tester for a motor vehicle that has a video coding device.
In order to be able to store an image sequence representing a succession of images, so as to have as few data as possible, video coding may involve provision for image compression in which image contents from a current image are described by indicating just the difference between the current image and a reference image (what is known as interprediction). In this case, a compression gain is usually obtained by virtue of more bits being necessary for indicating absolute color intensity values for an image than for coding the differences between the color intensity values of the reference image and of the current image. If an image sequence shows a moving object, this compression gain can be attained by virtue of matching image regions from the two compared images initially being found in order to determine the difference. If the object is situated in a top left corner in the reference image and in a bottom right corner in the current image, for example, because the object is moving diagonally through the image detail that is shown, then it makes no sense to calculate a difference between the top left corner of the reference image and simply the top left corner of the current image. Instead, an image processing device first of all needs to ascertain a change of position in the current image for objects depicted therein with respect to the reference image. The motion recognized in this process can be described in different ways. By way of example, the model parameters can be ascertained for a previously stipulated motion model. For this purpose, the RANSAC algorithm (RANSAC—random sample consensus) is known, for example. This involves the recognition of mapping parameters from a mapping specification for rotating and/or translationally moving image regions on the basis of striking image features. These image features are depictions of features of the objects that are shown both in the current image and in the reference image. In order to be able to detect such features and associate them with one another, the actual RANSAC algorithm needs to be preceded by processing, for example by a SIFT algorithm (SIFT—scale-invariant feature transform) or a SURF algorithm (SURF—Speeded Up Robust Features). Such feature detection and matching delivers feature pairs each comprising a feature from the reference image and a feature from the current image that correspond to one another optically. Usually, a predetermined number of feature pairs is provided.
The feature detection and association and a subsequent calculation of model parameters for recognizing the motion of the objects usually works efficiently only when only the motion of a very large object or similar motions of a large number of small objects have caused the alteration in the current image, however. If instead a plurality of objects move in different directions, it is very difficult firstly to find suitable feature pairs and secondly to recognize the different motions from the features. If this fails, features of moving objects cannot be correctly associated with one another. Accordingly, in connection with the coding of image sequences, as produced by a navigation appliance for a screen display in a motor vehicle, a relatively small compression gain has been established. Such image sequences are desirable to record for test purposes. The image sequences are distinguished in that they usually show a map region that is rotated on the basis of how the motor vehicle is currently oriented with respect to a north/south axis, for example. At the same time, however, the screens also show status bars and menus, which always remain at the same position in the image regardless of the orientation of the motor vehicle. The rotating map regions on the one hand and the status bars and menus on the other hand are objects that execute different motions in the image sequence.
In this regard, a technical paper by Zuliani et al (Zuliani, Kenney, Manjunath: “The MULTIRANSAC algorithm and its application to detect planar homographies”, Proc. IEEE International Conference on Image Processing, Genova, Italy, September 2005) describes a multimodel RANSAC approach in which different mapping parameters are determined for individual objects in a plurality of iterations. The correct segmentation of the individual objects is not always possible, however. The success of the multimodel RANSAC approach is highly dependent on whether the first iteration has delivered correct results. In addition, the computation complexity for ascertaining the mapping parameters is very high.